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Asymptotic distributions for quasi-efficient estimators in echelon VARMA models

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  • Dufour, Jean-Marie
  • Jouini, Tarek

Abstract

Two linear estimators for stationary invertible vector autoregressive moving average (VARMA) models in echelon form — to achieve parameter unicity (identification) — with known Kronecker indices are studied. It is shown that both estimators are consistent and asymptotically normal with strong innovations. The first estimator is a generalized-least-squares (GLS) version of the two-step ordinary least-squares (OLS) estimator studied in Dufour and Jouini (2005). The second is an asymptotically efficient estimator which is computationally much simpler than the Gaussian maximum-likelihood (ML) estimator which requires highly nonlinear optimization, and “efficient linear estimators” proposed earlier (Hannan and Kavalieris, 1984; Reinsel et al., 1992; Poskitt and Salau, 1995). It stands for a new relatively simple three-step estimator based on a linear regression involving innovation estimates which take into account the truncation error of the first-stage long autoregression. The complex dynamic structure of associated residuals is then exploited to derive an efficient covariance matrix estimator of the VARMA innovations, which is of order T−1 more accurate than the one by the fourth-stage of Hannan and Kavalieris’ procedure. Finally, finite-sample simulation evidence shows that, overall, the asymptotically efficient estimator suggested outperforms its competitors in terms of bias and mean squared errors (MSE) for the models studied.

Suggested Citation

  • Dufour, Jean-Marie & Jouini, Tarek, 2014. "Asymptotic distributions for quasi-efficient estimators in echelon VARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 69-86.
  • Handle: RePEc:eee:csdana:v:73:y:2014:i:c:p:69-86
    DOI: 10.1016/j.csda.2013.11.002
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    References listed on IDEAS

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    1. Gallego, Jose L., 2009. "The exact likelihood function of a vector autoregressive moving average process," Statistics & Probability Letters, Elsevier, vol. 79(6), pages 711-714, March.
    2. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
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    5. DUFOUR, Jean-Marie & JOUINI, Tarek, 2005. "Asymptotic Distribution of a Simple Linear Estimator for VARMA Models in Echelon Form," Cahiers de recherche 10-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    6. Athanasopoulos, George & Vahid, Farshid, 2008. "VARMA versus VAR for Macroeconomic Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 237-252, April.
    7. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
    8. Maravall, Agustin, 1993. "Stochastic linear trends : Models and estimators," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 5-37, March.
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    10. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
    11. Konstantinos Metaxoglou & Aaron Smith, 2007. "Maximum Likelihood Estimation of VARMA Models Using a State‐Space EM Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 666-685, September.
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    Cited by:

    1. Guy Melard, 2020. "An Indirect Proof for the Asymptotic Properties of VARMA Model Estimators," Working Papers ECARES 2020-10, ULB -- Universite Libre de Bruxelles.
    2. Dias, Gustavo Fruet & Kapetanios, George, 2018. "Estimation and forecasting in vector autoregressive moving average models for rich datasets," Journal of Econometrics, Elsevier, vol. 202(1), pages 75-91.

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